Human well-being outcomes of large-scale marine protected areas
Bibliographic record
Abstract
Large-scale marine protected areas (LSMPAs, >100,000 km 2 ) account for over half of the global ocean under protection, yet little is known about their outcomes for people. We conducted a review of the peer-reviewed literature to identify studies that investigated human well-being outcomes from forty-four (44) LSMPAs worldwide. Sixty-four (64) peer-reviewed articles were identified, which analyzed well-being outcomes in 18 of the 44 LSMPAs. For LSMPAs where human well-being outcomes have been studied, outcomes were highly variable and LSMPAs with more than three studies had both positive and negative outcomes. Fifty-two (52) percent of human well-being outcomes reported were positive, while 42 percent were negative, and 6 percent showed no change. Results highlight diverse domains of human well-being as the subject of studies, indicating that as increasing attention is placed on human well-being and MPAs, more aspects of social outcomes are being investigated. However, the scientific literature also left important variables of human well-being understudied, including the differentiated outcomes that LSMPAs can impart on race, gender, social class, and diverse cultural groups. Our review is a first step towards synthesizing existing knowledge but highlighted that our understanding is nascent. Future studies are needed that focus on understanding the differentiated impacts of LSMPAs across different social groups and that examine the processes that lead to different human well-being outcomes. With global commitments to protect 30 % of the oceans driving ongoing interest in LSMPA establishment, it is crucial to gain a better empirical understanding of the effect of LSMPAs on human well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".